a philosopher's sketchbook

Tag Archives: William Bernstein

I just finished reading The Intelligent Asset Allocator, by William Bernstein. The man knows his stuff. He has the statistics, data, and analysis to wow any investor who is interested in paying the man to invest on their behalf. This is probably why he’s quit his day job as a neurologist, and now advises a handful of wealthy clients on their investment portfolios.

What I find most interesting about the book is the disconnect between his historical analysis of asset market prices and his statistical analysis of portfolio asset allocations. After demonstrating how to perform a mean-variance analysis on a particular portfolio, or after demonstrating how to analyze the return-risk correlation in a binary relationship between two asset classes, the eponymous clause gets dropped: “past returns may not be indicative of future results.” I take this to support Bernstein’s distinction between three types of optimized portfolio asset allocations: historical, hypothetical, and future. The historical optimized asset allocation for a portfolio is possibly useless to us, since we do not invest in the past, and the future optimized asset allocation cannot be known without a working crystal ball. Mine’s been broken for years, the piece of junk. This leaves us with the hypothetical optimized asset allocation, which is not an actual number; rather, it is a best guess derived from, what is hopefully, a large pool of historical returns. This is only slightly better than the historical optimization described earlier. In other words, you can do all the calculations that you want with the best databases that you can find, but at the end of the day, you’re going to have to invest your money somehow, and the market will likely misbehave for you one way or another. Bernstein describes this problem well, “The investor’s objective, then, is not to find the efficient frontier (i.e. the most optimal asset allocation); that is impossible. Rather, the goal of the intelligent asset allocator is to find a portfolio mix that will come reasonably close to the mark under a broad range of circumstances.” The solution to this problem is only as complicated as one wishes to make it.

To be clear, I am not discounting Bernstein’s statistical methods. Rather, I am discounting the degree to which one might apply these methods. At the outset of the book, Bernstein recommends putting your money equally into four different index funds if you don’t have the patience to read this book carefully — 25% each for S&P500, US Small Cap, EAFE, and 5-year treasuries. He offers more meticulous portfolio allocations later in the book, with the caveat that they are not appropriate for all types of investment accounts, such as taxable ones. Some taxable accounts are only suitable for three of the four index funds recommended at the beginning of the book! This is because minimizing one’s tax risk is often tantamount, or even paramount, to maximizing one’s investment returns. Ultimately, how one allocates their assets is as much determined by circumstance, as it is by statistical analysis.

I fully intend to re-read parts of this book before it goes back to the library, so I can better retain the mathematical analyses described in The Intelligent Asset Allocator. However, I don’t intend to let statistical analysis over-complicate a manageable and low-maintenance investment plan: pareto optimization is essential in this area of life!

P.S.
I’m struggling with a way to write the Olive Presser articles. How do I describe the method of reducing expenses and investing income without simply parroting what ERE, MMM, and BNL have already written? The subject is not that complicated, and they have covered it so well! Never fear — I have traction on the issue, and the article is underway.